import org.apache.spark.sql.functions._
import org.apache.spark.sql._
import org.apache.spark.sql.types._
val spark: SparkSession = SparkSession.builder().
appName("myapp").master("local").getOrCreate()

case class Person(id: Int, date: String)
import spark.implicits._

val mydf: DataFrame = Seq(Person(1,"9/16/13")).toDF()
val select_df: DataFrame = mydf.select(unix_timestamp(mydf("date"),"MM/dd/yy").cast(TimestampType))
select_df.select(year($"date")).show()

import org.apache.spark.sql.functions._
import org.apache.spark.sql.types._
val df=Seq(("2019-07-19")).toDF("date")

//all the below actions results same i.e. year extracting from date column.
df.select(year('date).alias("new_date")).show()
df.select(year(df("date")).alias("new_date")).show()
df.select(year($"date").alias("new_date")).show()
df.withColumn("new_date",year('date)).select("new_date").show()

+--------+
|new_date|
+--------+
|    2019|
+--------+

case class Person(id: Int, date: String)
val mydf = Seq(Person(1,"9/16/13")).toDF
val solution = mydf.withColumn("year", year(to_timestamp($"date", "MM/dd/yy")))
scala> solution.show
+---+-------+----+
| id|   date|year|
+---+-------+----+
|  1|9/16/13|2013|
+---+-------+----+